Various methods and systems for controlling camera exposure settings are used in various vision-based applications, such as vehicle-occupant pattern-recognition applications. But vehicle lighting conditions are not always easily controllable in vehicle driving environments. For example, such environments include vastly differing and rapidly changing conditions, including total darkness, headlight flooding, sunny, cloudy, and shadowy lighting conditions.
The prior art includes several approaches for controlling exposure to account for such drastic changes in lighting. For example, many conventional exposure control methods involve image sophisticated histogram-based operations. But such operations require relatively high processing power and time to execute complex algorithms and, thus, may be too slow to carry out a dynamic occupant-sensing process. Other conventional exposure control methods involve special hardware designs to avert the need for complex algorithms, but at relatively high cost and complexity of camera components. Still other approaches use light-intensity detectors integrated with a camera system for better exposure control. Usually, however, such conventional methods provide only global exposure control, and extreme intensity variations in the background of an image tend to negatively affect the effectiveness of the exposure control system.